Radiation therapy can involve administering a dose of radiation to a human or animal subject. Careful planning can help ensure that the radiation reaches a target region of interest, while avoiding one or more nearby regions that are not expected to benefit from radiation and that may be impacted by side-effects of such radiation.
Three-dimensional (3D) imaging data can be used to characterize the internal structure of a specimen, such as a human patient, such as to help plan radiation treatment in such a patient, such as to treat a tumor. Such 3D imaging data can be obtained, for example, from a magnetic resonance (MR) or computed tomography (CT) imaging device or other imaging modality such as 3D electron microscopy. For example, the 3D CT imaging data can include voxels representing imaging data of various densities. For example, 3D CT voxel data of tissue within the subject will represent a higher density than voxels representing air outside of the subject. Voxels corresponding to air within a body cavity (e.g., within the bronchial tubes, for example, will also exhibit less density than surrounding tissue. Bone tissue voxels will have a higher density than softer tissue voxels. In another example, the 3D MR imaging data can include “k-space” values representing, in a spatial frequency domain, MR imaging data information.
Image “reconstruction” is an example of an “inverse problem” of generating structural information (e.g., an image of anatomical structures being studied) from acquired 3D CT imaging data or acquired 3D MR imaging data. The imaging data can be (and usually is) noisy, which can make it difficult to accurately reconstruct an exact image of the interior of a patient. Image “segmentation” refers to partitioning a reconstructed image into multiple regions, such as can permit locating of boundaries between structures in the reconstructed image.